Patient-Adaptive Ectopic Beat Classification using Active Learning
Author(s)Wiens, J.; Guttag, John V.
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A major challenge in applying machine learning techniques to the problem of heartbeat classification is dealing effectively with inter-patient differences in electrocardiograms (ECGs). Inter-patient differences create a need for patient-specific classifiers, since there is no a priori reason to assume that a classifier trained on data from one patient will yield useful results when applied to a different patient. Unfortunately, patient-specific classifiers come at a high cost, since they require a labeled training set. Using active learning, we show that one can drastically reduce the amount of patient-specific labeled training data required to build a highly accurate patient-specific binary heartbeat classifier for identifying ventricular ectopic beats. Tested on all 48 half-hour ECG recordings from the MIT-BIH Arrhythmia Database, our approach achieves an average sensitivity of 96.2% and specificity of 99.9%. The average number of beats needed to train each patient-specific classifier was less than 37 beats, approximately 30 seconds of data.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the 2010 Computing in Cardiology
Institute of Electrical and Electronics Engineers (IEEE)
J. Wiens, J.V. Guttag. "Patient-Adaptive Ectopic Beat Classification using Active Learning" Proceedings of the 2010 Computing in Cardiology, IEEE. © Copyright 2010 IEEE
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